Course Coordinates

Taught at the University of Konstanz by Hansjörg Neth (h.neth@uni.kn, SPDS, office D507).
Winter 2018/2019: Mondays, 13:30–15:00, C511.
Links to current course syllabus | ZeUS | Ilias

Artwork

Overview over generated plots:

Logo

Different ds4psy logo options:

Tiles

Showing the versions with borders:

Tile plots (random)

ds4psy ds4psy ds4psy ds4psy ds4psy

Tile plots (sorted)

ds4psy ds4psy ds4psy ds4psy ds4psy

Tile plots (with borders and numeric labels)

Random versions:

ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy

Sorted versions:

ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy

Poles

Showing the versions without borders:

Pole plots (random)

ds4psy ds4psy ds4psy ds4psy ds4psy

Pole plots (sorted)

ds4psy ds4psy ds4psy ds4psy ds4psy

Pole plots (with borders and numeric labels)

Random versions:

ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy

Sorted versions:

ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy ds4psy

Text

Text labels

# Define text labels: ------ 
course_title <- paste0("Data science for psychologists")
course_title_abb <- paste0("ds4psy")
psi <- expression(psi)

name_hn <- "Hansjörg Neth"
name_course <- paste0(name_hn, ":", " ", course_title)
# name_course

Course labels:

course_title = Data science for psychologists
course_title_abb = ds4psy
psi = psi

Colors

Color palettes

## Re-sort color palette: 
# unikn.pal  # from "ds4psy/R/custom_functions.R""

## Define colors and color palettes. 

## Define individual colors: ------

seeblau <- rgb(0, 169, 224, names = "seeblau", maxColorValue = 255) # seeblau.4 (non-transparent)

## Source: https://www.uni-konstanz.de/en/university/news-and-media/create-online-and-print-media/corporate-design/the-university-of-konstanzs-corporate-design/ 

## Define color palettes: ------

## (A) Web (sRGB) scale: ---- 

pal_unikn = data.frame(                                 ## as df: 
  "seeblau1" = rgb(204, 238, 249, maxColorValue = 255), #  1. seeblau1 (non-transparent)
  "seeblau2" = rgb(166, 225, 244, maxColorValue = 255), #  2. seeblau2 (non-transparent)
  "seeblau3" = rgb( 89, 199, 235, maxColorValue = 255), #  3. seeblau3 (non-transparent)
  "seeblau4" = rgb(  0, 169, 224, maxColorValue = 255), #  4. seeblau4 (= seeblau base color)
  "black"    = rgb(  0,   0,   0, maxColorValue = 255), #  5. black
  "seegrau4" = rgb(102, 102, 102, maxColorValue = 255), #  6. grey40 (non-transparent)
  "seegrau3" = rgb(153, 153, 153, maxColorValue = 255), #  7. grey60 (non-transparent)
  "seegrau2" = rgb(204, 204, 204, maxColorValue = 255), #  8. grey80 (non-transparent)
  "seegrau1" = rgb(229, 229, 229, maxColorValue = 255), #  9. grey90 (non-transparent)
  "white"    = rgb(255, 255, 255, maxColorValue = 255), # 10. white
  stringsAsFactors = FALSE)
# pal_unikn

# Note: Web (sRGB) colors appear to be more vibrant.

# Sensibly sorted version:
pal_unikn <- pal_unikn[c(4:1, 10:5)] # seeblau (1) > white (5) > grey > black (10)

### Check:
# pal_unikn
# # => 
# #   seeblau4 seeblau3 seeblau2 seeblau1   white seegrau1 seegrau2 seegrau3 seegrau4   black
# # 1  #00A9E0  #59C7EB  #A6E1F4  #CCEEF9 #FFFFFF  #E5E5E5  #CCCCCC  #999999  #666666 #000000

## (B) PowerPoint scale: ---- 

pal_unikn_ppt = data.frame(                                 ## as df: 
  "seeblau1" = rgb(200, 229, 239, maxColorValue = 255), #  1. seeblau1 (non-transparent)
  "seeblau2" = rgb(160, 211, 230, maxColorValue = 255), #  2. seeblau2 (non-transparent)
  "seeblau3" = rgb( 89, 182, 220, maxColorValue = 255), #  3. seeblau3 (non-transparent)
  "seeblau4" = rgb(  0, 154, 209, maxColorValue = 255), #  4. seeblau4 (= seeblau base color)
  "black"    = rgb(  0,   0,   0, maxColorValue = 255), #  5. black
  "seegrau4" = rgb(102, 102, 102, maxColorValue = 255), #  6. grey40 (non-transparent)
  "seegrau3" = rgb(153, 153, 153, maxColorValue = 255), #  7. grey60 (non-transparent)
  "seegrau2" = rgb(204, 204, 204, maxColorValue = 255), #  8. grey80 (non-transparent)
  "seegrau1" = rgb(229, 229, 229, maxColorValue = 255), #  9. grey90 (non-transparent)
  "white"    = rgb(255, 255, 255, maxColorValue = 255), # 10. white
  stringsAsFactors = FALSE)

# Note: PowerPoint colors appear to be more muted, less vibrant.

# Sensibly sorted version:
pal_unikn_ppt <- pal_unikn_ppt[c(4:1, 10:5)] # seeblau (1) > white (5) > grey > black (10)

### Check:
# pal_unikn_ppt
# # => 
# #   seeblau4 seeblau3 seeblau2 seeblau1   white seegrau1 seegrau2 seegrau3 seegrau4   black
# # 1  #009AD1  #59B6DC  #A0D3E6  #C8E5EF #FFFFFF  #E5E5E5  #CCCCCC  #999999  #666666 #000000

## Function to get (n x n) colors of a palette: ------ 

col_n_sq <- function(n = 10, pal = pal_unikn){
  
  out <- NA
  
  if (n == 1) {
    
    out <- pal[1] #  1 color: seeblau4
    
  } else if (n == 2) {
    
    out <- pal[c(1, 3, 5, 10)]   #  4 colors: seeblau4, seeblau2, white, black
    
  } else if (n == 3) {
    
    out <- pal[-7] #  9 colors: seeblau > white > black
    
  } else {
    
    out <- pal[c(1, 1:10)]   # 11 colors: seeblau (2x) > white (6 = mid) > black (11) [default]
    
  }
  
  return(out)
  
}

## Check:
# col_n_sq(1)
# col_n_sq(2)
# col_n_sq(3)
# col_n_sq(4)

# Reset:
unikn.pal <- pal_unikn
unikn.pal
#>   seeblau4 seeblau3 seeblau2 seeblau1   white seegrau1 seegrau2 seegrau3
#> 1  #00A9E0  #59C7EB  #A6E1F4  #CCEEF9 #FFFFFF  #E5E5E5  #CCCCCC  #999999
#>   seegrau4   black
#> 1  #666666 #000000

Plot themes

ds_theme <- theme_bw() + 
  theme(panel.grid = element_blank(),
        legend.position = "none",
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        panel.background = element_rect(fill = "white"),
        panel.border = element_rect(color = grey(.25, 1)))

ds_theme <- theme_nothing()

## ToDo: Define a generic ds4psy ggplot theme.

theme_ds4psy <- ds_theme

Logo

Corresponding logo:

# Define some positions: ------

dx <- 1
dy <- 1
dd <- 1

# Table: ------

tb_ds4p <- tibble::tribble(
  ~sym, ~x, ~y,
  "d",   -dx,  +dy,
  "s",   +dx,  +dy,
  "4",   -dx,  -dy,
  "psy", +dx,  -dy )
tb_ds4p$row <- 1:nrow(tb_ds4p)
tb_ds4p
#> # A tibble: 4 x 4
#>   sym       x     y   row
#>   <chr> <dbl> <dbl> <int>
#> 1 d        -1     1     1
#> 2 s         1     1     2
#> 3 4        -1    -1     3
#> 4 psy       1    -1     4

# Colors: ------ 

## Based on pal_unikn:

pal_unikn <- unikn.pal

# col_4 <- col_n_sq(n = 2, pal_unikn)
# col_4
# => 
#   seeblau4 seeblau2   white   black
# 1  #00A9E0  #A6E1F4 #FFFFFF #000000

col_4 <- pal_unikn[c("seeblau2", "seeblau4", "white", "black")]
# col_4
# => 
#   seeblau4 seeblau2   white   black
# 1  #00A9E0  #A6E1F4 #FFFFFF #000000

seegrau1 <- pal_unikn[["seegrau1"]] 
# seegrau1
# => 
#   seegrau1
# 1  #E5E5E5

# Parameters: ------ 

txt_size <- 5.00
c_shift  <- 0.80

# Plot: ------

plot_plot <- TRUE

if (plot_plot) {
  
  logo <- ggplot2::ggplot(tb_ds4p) +
    geom_tile(aes(x = x, y = y, fill = row)) + 
    # Add points: 
    geom_point(x = (+dx + c_shift * dx), y = (dy + c_shift * dy), pch = 4, size = 1, col = "white") +  # mark coordinate with "x"
    # geom_point(x = (-dx), y = (-dy), pch = 16, size = 11, col = seegrau1) +                          # mark coordinate with "O"
    # Add text: 
    geom_text(aes(x = x, y = y, label = sym), col = grey(0.0, 1.0), size = txt_size, family = "Arial", fontface = 2, adj = .5) +  # 4 text elements
    geom_text(x = dx, y = -dy, label = expression(psi), col = grey(1.0, 1.0), size = (txt_size + 1), family = "Arial", fontface = 2, adj = .5) +    # psi in lower right  
    # Set scales:
    scale_x_continuous(limits = c((-dx - dd), (+dx + dd))) +
    scale_y_continuous(limits = c((-dy - dd), (+dy + dd))) +
    scale_fill_gradientn(colors = col_4) + 
    coord_fixed() +
    theme_ds4psy
  logo
  
  ## Save plot: ------ 
  
  plot_name <- "logo"
  full_name <- paste0(plot_out, plot_name, ".png")
  
  ggsave(full_name, width = 2, height = 2, units = c("cm"), dpi = 300)
  
} # if (plot_plot) etc.

Theme

ToDo: Create a dedicated ds4psy ggplot theme.

Leet slang (l33t)

See https://simple.wikipedia.org/wiki/Leet for details.

## Using leet / l33t slang: ------
                             # l33t rul35: 
n4me <- "h4n5j03Rg n3+h"     # e:3, a:4, s:5, o:0, t:+, r:R
d5   <- "d4+4 5c13nc3"       # i:1 
fp   <- "f0R p5ych0l0g15+5"
course_l33t <- paste0(n4me, ":", " ", d5, " ", fp)
course_l33t
#> [1] "h4n5j03Rg n3+h: d4+4 5c13nc3 f0R p5ych0l0g15+5"

## Automation: ----- 

txt  <- "This is a short test string with some text to leetify."
txt2 <- "Data science is both a craft and an art. This course introduces fundamental data types, basic concepts and commands of the R programming language, and explores key packages of the so-called tidyverse. Regular exercises will help you to make your first steps from R novice to user."

# Write functions to:  
# - switch text to lower/upper case, capitalize, etc.
# - leetify a string of text (with sets of rules)
# - mix content (letters, words, ...) with noise (punctuation, space, random characters)

# l33t rules: ------ 
l33t_num <- c("a" = "4", "A" = "4", 
              "e" = "3", "E" = "3", 
              "i" = "1", "I" = "1", 
              "o" = "0", "O" = "0", 
              "s" = "5", "S" = "5", 
              "T" = "7"
              )

my_l33t <- c("t" = "+",
             "r" = "R"
             ) 

l33t_rul35 <- c(l33t_num, my_l33t)
l33t_rul35
#>   a   A   e   E   i   I   o   O   s   S   T   t   r 
#> "4" "4" "3" "3" "1" "1" "0" "0" "5" "5" "7" "+" "R"

## l33t function: ----- 
str_replace_all(txt, l33t_rul35)
#> [1] "7h15 15 4 5h0R+ +35+ 5+R1ng w1+h 50m3 +3x+ +0 l33+1fy."

transl33t <- function(txt, rules = l33t_rul35,
                      in_case = "no", out_case = "no") {
  
  # robustness: 
  in_case  <- tolower(substr(in_case,  1 , 2))  # 1st 2 letters of in_case
  out_case <- tolower(substr(out_case, 1 , 2))  # 1st 2 letters of out_case  
  
  # handle in_case: 
  if (in_case == "lo") {
    txt <- tolower(txt)
  } else if (in_case == "up") {
    txt <- toupper(txt)
  }

  # transl33t based on rules:   
  out <- str_replace_all(txt, rules)
  
  # handle out_case: 
  if (out_case == "lo") {
    out <- tolower(out)
  } else  if (out_case == "up") {
    out <- toupper(out)
  }
  
  return(out)
  
}

## Check: 
transl33t(txt, rules = c("a" = "4"))
#> [1] "This is 4 short test string with some text to leetify."
transl33t(txt)
#> [1] "7h15 15 4 5h0R+ +35+ 5+R1ng w1+h 50m3 +3x+ +0 l33+1fy."
transl33t(txt = c(txt, txt2))
#> [1] "7h15 15 4 5h0R+ +35+ 5+R1ng w1+h 50m3 +3x+ +0 l33+1fy."                                                                                                                                                                                                                                  
#> [2] "D4+4 5c13nc3 15 b0+h 4 cR4f+ 4nd 4n 4R+. 7h15 c0uR53 1n+R0duc35 fund4m3n+4l d4+4 +yp35, b451c c0nc3p+5 4nd c0mm4nd5 0f +h3 R pR0gR4mm1ng l4ngu4g3, 4nd 3xpl0R35 k3y p4ck4g35 0f +h3 50-c4ll3d +1dyv3R53. R3gul4R 3x3Rc1535 w1ll h3lp y0u +0 m4k3 y0uR f1R5+ 5+3p5 fR0m R n0v1c3 +0 u53R."
transl33t(txt = c(letters, LETTERS))
#>  [1] "4" "b" "c" "d" "3" "f" "g" "h" "1" "j" "k" "l" "m" "n" "0" "p" "q"
#> [18] "R" "5" "+" "u" "v" "w" "x" "y" "z" "4" "B" "C" "D" "3" "F" "G" "H"
#> [35] "1" "J" "K" "L" "M" "N" "0" "P" "Q" "R" "5" "7" "U" "V" "W" "X" "Y"
#> [52] "Z"

# 9 variants:
transl33t(txt)  # leave in_case and out_case as is.
#> [1] "7h15 15 4 5h0R+ +35+ 5+R1ng w1+h 50m3 +3x+ +0 l33+1fy."
transl33t(txt,  in_case = "lo")
#> [1] "+h15 15 4 5h0R+ +35+ 5+R1ng w1+h 50m3 +3x+ +0 l33+1fy."
transl33t(txt,  in_case = "up")
#> [1] "7H15 15 4 5H0R7 7357 57R1NG W17H 50M3 73X7 70 L3371FY."
transl33t(txt, out_case = "lo")
#> [1] "7h15 15 4 5h0r+ +35+ 5+r1ng w1+h 50m3 +3x+ +0 l33+1fy."
transl33t(txt, out_case = "up")
#> [1] "7H15 15 4 5H0R+ +35+ 5+R1NG W1+H 50M3 +3X+ +0 L33+1FY."
transl33t(txt,  in_case = "lo", out_case = "lo")
#> [1] "+h15 15 4 5h0r+ +35+ 5+r1ng w1+h 50m3 +3x+ +0 l33+1fy."
transl33t(txt,  in_case = "lo", out_case = "up")
#> [1] "+H15 15 4 5H0R+ +35+ 5+R1NG W1+H 50M3 +3X+ +0 L33+1FY."
transl33t(txt,  in_case = "up", out_case = "lo")
#> [1] "7h15 15 4 5h0r7 7357 57r1ng w17h 50m3 73x7 70 l3371fy."
transl33t(txt,  in_case = "up", out_case = "up")
#> [1] "7H15 15 4 5H0R7 7357 57R1NG W17H 50M3 73X7 70 L3371FY."

Data

Table tb with sorted and random series from 1 to N values (and corresponding colors, etc.):

# Dimensions:
n_rand <- sample(1:15, size = 1, replace = TRUE)  # random integer (1:15)

n_x <- n_rand  # random number
n_x <- 2     # a specific number
n_y <- n_x
N <- (n_x * n_y)

# Vectors: 
v_sort <- 1:N         # Tile: top_left = seeblau, bottom_right = black   | Polar: outer = seeblau, center = black.
# v_sort <- rev(1:N)  # Tile: top_left = black,   bottom_right = seeblau | Polar: outer = black, center = seeblau.

# Colors for current n_x:
unikn_sort <- col_n_sq(n_x)
unikn_sort 
#>   seeblau4 seeblau2   white   black
#> 1  #00A9E0  #A6E1F4 #FFFFFF #000000

col_sort <- rep("white", N)  # default

lim_black <- .245  # threshold to switch from "white" to "black" labels
col_sort[(v_sort > (lim_black * N)) & (v_sort < ((1 - lim_black) * N))] <- "black"
# table(col_sort)


set.seed(815)  # for reproducible randomness

# v_rand <- runif(n = N, 0, 1)
rand_ord <- sample(v_sort, N)  # random permutation of v_sort
v_rand <- rand_ord  # random permutation of v_sort
col_rand <- col_sort[rand_ord]


# Table: 
tb <- tibble(x = rep(1:n_y, times = n_x),
             y = rep(n_x:1, each = n_y),
             sort = v_sort,
             rand = v_rand,
             col_sort = col_sort,
             col_rand = col_rand
)
tb
#> # A tibble: 4 x 6
#>       x     y  sort  rand col_sort col_rand
#>   <int> <int> <int> <int> <chr>    <chr>   
#> 1     1     2     1     1 black    black   
#> 2     2     2     2     4 black    white   
#> 3     1     1     3     3 black    black   
#> 4     2     1     4     2 white    black

More data: See table of session topics in toc below.

Tiles

Tile plots

With thin borders:

# Parameters: 
brd_col <- grey(0, 1)
brd_size <- .10

plot_size <-  3.0  # NORMAL: in cm (used in ggsave below): normal (small) size
# plot_size <- 10.0  # BIG:    in cm (used in ggsave below): when "pix/big_"
lbl_size <- 1.5

# Tile plots: ------ 

# (1a) random version (WITH border lines):
tile_rand <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = rand), col = brd_col, size = brd_size) +
  # geom_text(aes(x = x, y = y, label = rand), col = col_rand, size = lbl_size) +  # with tile labels 
  coord_fixed() + 
  # labs(title = "ds4psy") + 
  labs(x = "Data", y = "Science") +
  ## Color schemes: 
  # scale_fill_continuous(low = seeblau, high = grey(1, 1)) +   # s1: light: seeblau > white
  scale_fill_gradientn(colors = unikn_sort) +                   # s2: full unikn_sort palette: seeblau > white > black [default]
  # scale_fill_gradientn(colors = c(unikn_sort, "gold")) +      # s3: highlight final row
  ds_theme
tile_rand


# Save current plot: 
cur_name <- paste0("tile_rand_", n_x, "_brd.png")
if (plot_size < 10) {
  plot_name <- paste0(pic_path, "tile/", cur_name)  # insert sub-dir "tile"
} else {
  plot_name <- paste0(pic_path, "big/", "big_", cur_name)  # sub-dir "big" and insert "big_" prefix
}
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

# (1b) sorted version (WITH border lines):
tile_sort <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = sort), col = brd_col, size = brd_size) +
  # geom_text(aes(x = x, y = y, label = sort), col = col_sort, size = lbl_size) +  # with tile labels
  # coord_polar() +
  coord_fixed() + 
  # labs(title = "ds4psy") + 
  labs(x = "4", y = expression(psi)) +
  ## Color schemes: 
  # scale_fill_continuous(low = seeblau, high = grey(1, 1)) +   # s1: light: seeblau > white
  scale_fill_gradientn(colors = unikn_sort) +                   # s2: full unikn_sort palette: seeblau > white > black [default]
  # scale_fill_gradientn(colors = c(unikn_sort, "gold")) +      # s3: highlight final row
  ds_theme
tile_sort


# Save current plot: 
cur_name <- paste0("tile_sort_", n_x, "_brd.png")
if (plot_size < 10) {
  plot_name <- paste0(pic_path, "tile/", cur_name)
} else {
  plot_name <- paste0(pic_path, "big/", "big_", cur_name)  # insert "big_" prefix
}
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

Without thin borders:

# Tile plots: ------ 

# (2a) random version (withOUT border lines):
tile_rand <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = rand))+ #, col = brd_col, size = brd_size) +
  coord_fixed() + 
  # labs(title = "ds4psy") + 
  labs(x = "Data", y = "Science") +
  ## Color schemes: 
  # scale_fill_continuous(low = seeblau, high = grey(1, 1)) +   # s1: light: seeblau > white
  scale_fill_gradientn(colors = unikn_sort) +                   # s2: full unikn_sort palette: seeblau > white > black [default]
  # scale_fill_gradientn(colors = c(unikn_sort, "gold")) +      # s3: highlight final row
  ds_theme
tile_rand


# Save current plot: 
cur_name <- paste0("tile_rand_", n_x, ".png")
if (plot_size < 10) {
  plot_name <- paste0(pic_path, "tile/", cur_name)
} else {
  plot_name <- paste0(pic_path, "big/", "big_", cur_name)  # insert "big_" prefix
}

ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

# (2b) sorted version (withOUT border lines):

tile_sort <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = sort))+ #, col = brd_col, size = brd_size) +
  # coord_polar() +
  coord_fixed() + 
  # labs(title = "ds4psy") + 
  labs(x = "4", y = expression(psi)) +
  ## Color schemes: 
  # scale_fill_continuous(low = seeblau, high = grey(1, 1)) +   # s1: light: seeblau > white
  scale_fill_gradientn(colors = unikn_sort) +                   # s2: full unikn_sort palette: seeblau > white > black [default]
  # scale_fill_gradientn(colors = c(unikn_sort, "gold")) +      # s3: highlight final row
  ds_theme
tile_sort


# Save current plot: 
cur_name <- paste0("tile_sort_", n_x, ".png")
if (plot_size < 10) {
  plot_name <- paste0(pic_path, "tile/", cur_name)
} else {
  plot_name <- paste0(pic_path, "big/", "big_", cur_name)  # insert "big_" prefix
}

ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

With thin borders and a numeric label (top left):

# Parameters:
cur_lbl <- paste0(n_x)

x_lbl <- 1 
y_lbl <- (n_y + 1) + n_y/15

# Tile plots: ------ 

# (3a) random version (WITH border lines AND label):
tile_rand_lbl <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = rand), col = brd_col, size = brd_size) +
  geom_text(x = x_lbl, y = y_lbl, label = cur_lbl, size = 2) +
  scale_y_continuous(limits = c(0, y_lbl)) + 
  # geom_text(x = 0, y = n_y, label = cur_lbl, size = 2) +
  # scale_x_continuous(limits = c(0, n_x + 1)) + 
  coord_fixed() + 
  # labs(title = "ds4psy") + 
  labs(x = "Data", y = "Science") +
  ## Color schemes: 
  # scale_fill_continuous(low = seeblau, high = grey(1, 1)) +   # s1: light: seeblau > white
  scale_fill_gradientn(colors = unikn_sort) +                   # s2: full unikn_sort palette: seeblau > white > black [default]
  # scale_fill_gradientn(colors = c(unikn_sort, "gold")) +      # s3: highlight final row
  ds_theme
tile_rand_lbl


# Save current plot: 
cur_name <- paste0("tile_rand_", n_x, "_brd_lbl.png")
if (plot_size < 10) {
  plot_name <- paste0(pic_path, "tile/", cur_name)
} else {
  plot_name <- paste0(pic_path, "big/", "big_", cur_name)  # insert "big_" prefix
}
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)


# (3b) sorted version (WITH border lines AND label):
tile_sort_lbl <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = sort), col = brd_col, size = brd_size) +
  geom_text(x = x_lbl, y = y_lbl, label = cur_lbl, size = 2) +
  scale_y_continuous(limits = c(0, y_lbl)) + 
  # geom_text(x = 0, y = n_y, label = cur_lbl, size = 2) +
  # scale_x_continuous(limits = c(0, n_x + 1)) + 
  coord_fixed() + 
  # labs(title = "ds4psy") + 
  labs(x = "Data", y = "Science") +
  ## Color schemes: 
  # scale_fill_continuous(low = seeblau, high = grey(1, 1)) +   # s1: light: seeblau > white
  scale_fill_gradientn(colors = unikn_sort) +                   # s2: full unikn_sort palette: seeblau > white > black [default]
  # scale_fill_gradientn(colors = c(unikn_sort, "gold")) +      # s3: highlight final row
  ds_theme
tile_sort_lbl


# Save current plot: 
cur_name <- paste0("tile_sort_", n_x, "_brd_lbl.png")
if (plot_size < 10) {
  plot_name <- paste0(pic_path, "tile/", cur_name)
} else {
  plot_name <- paste0(pic_path, "big/", "big_", cur_name)  # insert "big_" prefix
}
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

ToDo:

In sorted tile plots:

Change order of sorted tiles (so that black is at bottom right corner).
Consider highlighting one row in a different color (e.g., gold to orange).
Consider adding 1 differently colored tile (e.g., draw bottom right corner in gold or red color).

Polar plots

With thin borders:

# Polar plots: ------ 

pole_rand <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = rand), col = brd_col, size = brd_size) +
  # labs(title = "ds4psy") + 
  coord_polar() + 
  labs(x = "Data", y = "Science") +
  ## Color schemes: 
  # scale_fill_continuous(low = seeblau, high = grey(1, 1)) +   # s1: light: seeblau > white
  scale_fill_gradientn(colors = unikn_sort) +                   # s2: full unikn_sort palette: seeblau > white > black [default]
  # scale_fill_gradientn(colors = c(unikn_sort, "gold")) +      # s3: highlight final row
  ds_theme
pole_rand


# Save current plot: 
plot_name <- paste0("pix/pole/pole_rand_", n_x, "_brd.png")
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

pole_sort <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = sort), col = brd_col, size = brd_size) +
  labs(x = "4", y = expression(psi)) +
  coord_polar() +
  ## Color schemes: 
  # scale_fill_continuous(low = seeblau, high = grey(1, 1)) +   # s1: light: seeblau > white
  scale_fill_gradientn(colors = unikn_sort) +                   # s2: full unikn_sort palette: seeblau > white > black [default]
  # scale_fill_gradientn(colors = c(unikn_sort, "gold")) +      # s3: highlight final row
  ds_theme
pole_sort


# Save current plot: 
plot_name <- paste0("pix/pole/pole_sort_", n_x, "_brd.png")
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

Without thin borders:

# Polar plots: ------ 

pole_rand <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = rand)) + # , col = brd_col, size = brd_size) +
  # labs(title = "ds4psy") + 
  coord_polar() + 
  labs(x = "Data", y = "Science") +
  ## Color schemes: 
  # scale_fill_continuous(low = seeblau, high = grey(1, 1)) +   # s1: light: seeblau > white
  scale_fill_gradientn(colors = unikn_sort) +                   # s2: full unikn_sort palette: seeblau > white > black [default]
  # scale_fill_gradientn(colors = c(unikn_sort, "gold")) +      # s3: highlight final row
  ds_theme
pole_rand


# Save current plot: 
plot_name <- paste0("pix/pole/pole_rand_", n_x, ".png")
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

pole_sort <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = sort)) + # , col = brd_col, size = brd_size) +
  labs(x = "4", y = expression(psi)) +
  coord_polar() +
  ## Color schemes: 
  # scale_fill_continuous(low = seeblau, high = grey(1, 1)) +   # s1: light: seeblau > white
  scale_fill_gradientn(colors = unikn_sort) +                   # s2: full unikn_sort palette: seeblau > white > black [default]
  # scale_fill_gradientn(colors = c(unikn_sort, "gold")) +      # s3: highlight final row
  ds_theme
pole_sort


# Save current plot: 
plot_name <- paste0("pix/pole/pole_sort_", n_x, ".png")
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

With thin borders and a numeric label (of the final slice):

# Parameters:
cur_lbl <- paste0(n_x)

if (n_y == 1) {
  y_lbl <- n_y + 0     # no correction
} else if (n_y == 2) {
  y_lbl <- n_y + .75   # small correction
} else if (n_y < 4) {
  y_lbl <- n_y + 1     # constant correction
} else {
 y_lbl <- n_y + n_y/4  # scaled correction (increasing with n_y)
}

# (a) rand version with label:
pole_rand_lbl <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = rand), col = brd_col, size = brd_size) +
  # labs(title = "ds4psy") + 
  # scale_x_continuous(breaks = c(0:n_x), labels = c(0:n_x)) +
  # geom_vline(xintercept = n_x, size = 1, color = "gold") + 
  geom_text(x = n_x, y = y_lbl, label = cur_lbl, size = 2) + 
  coord_polar() + 
  labs(x = "Data", y = "Science") +
  ## Color schemes: 
  # scale_fill_continuous(low = seeblau, high = grey(1, 1)) +   # s1: light: seeblau > white
  scale_fill_gradientn(colors = unikn_sort) +                   # s2: full unikn_sort palette: seeblau > white > black [default]
  # scale_fill_gradientn(colors = c(unikn_sort, "gold")) +      # s3: highlight final row
  ds_theme
pole_rand_lbl


# Save current plot: 
plot_name <- paste0("pix/pole/pole_rand_", n_x, "_brd_lbl.png")
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

# (a) sort version with label:
pole_sort_lbl <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = sort), col = brd_col, size = brd_size) +
  # labs(title = "ds4psy") + 
  # scale_x_continuous(breaks = c(0:n_x), labels = c(0:n_x)) +
  # geom_vline(xintercept = n_x, size = 1, color = "gold") + 
  geom_text(x = n_x, y = y_lbl, label = cur_lbl, size = 2) + 
  coord_polar() + 
  labs(x = "Data", y = "Science") +
  ## Color schemes: 
  # scale_fill_continuous(low = seeblau, high = grey(1, 1)) +   # s1: light: seeblau > white
  scale_fill_gradientn(colors = unikn_sort) +                   # s2: full unikn_sort palette: seeblau > white > black [default]
  # scale_fill_gradientn(colors = c(unikn_sort, "gold")) +      # s3: highlight final row
  ds_theme
pole_sort_lbl


# Save current plot: 
plot_name <- paste0("pix/pole/pole_sort_", n_x, "_brd_lbl.png")
ggsave(plot_name, width = plot_size, height = plot_size, units = c("cm"), dpi = 300)

Pi tiles

Add the 1st N digits of pi as data to tb:

#> # A tibble: 4 x 6
#>       x     y  sort  rand col_sort col_rand
#>   <int> <int> <int> <int> <chr>    <chr>   
#> 1     1     2     1     1 black    black   
#> 2     2     2     2     4 black    white   
#> 3     1     1     3     3 black    black   
#> 4     2     1     4     2 white    black
#> [1] 4
#> [1] TRUE
#> [1] 3 1 4 1
#> # A tibble: 4 x 6
#>    sort  rand     x     y col_sort col_rand
#>   <int> <int> <int> <int> <chr>    <chr>   
#> 1     1     1     1     2 black    black   
#> 2     2     4     2     2 black    white   
#> 3     3     3     1     1 black    black   
#> 4     4     2     2     1 white    black
#> # A tibble: 4 x 9
#>    sort  rand     x     y col_sort col_rand pi_sort pi_rand col_pi
#>   <int> <int> <int> <int> <chr>    <chr>      <dbl>   <dbl> <chr> 
#> 1     1     1     1     2 black    black          3       3 black 
#> 2     2     4     2     2 black    white          1       1 white 
#> 3     3     3     1     1 black    black          4       4 black 
#> 4     4     2     2     1 white    black          1       1 white

Colors:

Graphical version:

The first 4 digits of pi mapped to uni.kn color scale:

0 is white
1 is black
2 is dark grey
3 is seeblau

# Parameters:
cur_lbl <- expression(pi)
lbl_size <- 2
x_lbl <- 1 
y_lbl <- (n_y + 1) + n_y/15

# Tile plots: ------ 

# tb  # data

# (4a) sorted version (WITH border lines AND label):
pi_tile_sort_lbl <- ggplot(tb) +
  geom_tile(aes(x = x, y = y, fill = pi_sort), col = brd_col, size = brd_size) +
  geom_text(aes(x = x, y = y, label = pi_sort), col = "black", size = lbl_size) +  # pi values as tile labels 
  scale_y_continuous(limits = c(0, y_lbl)) + 
  # scale_x_continuous(limits = c(0, n_x + 1)) + 
  coord_fixed() + 
  # labs(title = "ds4psy") + 
  ## Color schemes: 
  # scale_fill_continuous(low = seeblau, high = grey(1, 1)) +   # light: seeblau > white
  # scale_fill_gradientn(colors = unikn_sort) +               # full unikn_sort palette: seeblau > white > black
  # scale_fill_gradientn(colors = c(unikn_sort, "gold")) +    # highlight final row
  # scale_fill_gradientn(colors = c(unikn_sort, "gold")) +    # highlight final row
  scale_fill_gradientn(colors = unikn_pi) +
  ds_theme
pi_tile_sort_lbl

Combine multiple plots

plot_grid(tile_rand, tile_sort)

plot_grid(pole_rand, pole_sort)

plot_grid(tile_rand, pole_sort)

Waves

Data:

N <- 100000

df <- tibble(x = 1:N,
             a = rnorm(n = N, mean =   0, sd = 250),
             # b = rnorm(n = N, mean = 100, sd = 200), 
             c = rnorm(n = N, mean = 500, sd = 200)
)
# df

Density plots:

lwd_all <- 3

ggplot(df) +
  geom_density(aes(x = a), col = seeblau, fill = "black", lwd = lwd_all, alpha = .25) +
  # geom_density(aes(x = b), col = "black", fill = unikn.pal[[5]], alpha = .33) + 
  geom_density(aes(x = c), col = "black", fill = seeblau, lwd = lwd_all, alpha = .33) +
  theme_void()

Histograms (with function curves):

lwd_all <- .75

# overlay histogram and normal density
ggplot(df) +
  geom_histogram(aes(x = c, y = stat(density)), binwidth = 150, col = "white", fill = "black", alpha = .25) +
  geom_histogram(aes(x = a, y = stat(density)), binwidth = 150, col = "white", fill = seeblau, alpha = .25) +
  stat_function(fun = dnorm, args = list(mean = mean(df$c), sd = sd(df$c)), lwd = lwd_all, col = "black", alpha = .95) + 
  stat_function(fun = dnorm, args = list(mean = mean(df$a), sd = sd(df$a)), lwd = lwd_all, col = seeblau, alpha = .95) + 
  scale_x_continuous(limits = c(-2000, 1400)) +
  theme_void() # +

  # annotate(geom = "text", x = -1900, y = .0018, label = course_title, color = "black", alpha = .90, adj = 0, fontface = 2, size = 2.5)

ggsave("pix/wave/hist_wave_2.png", width = 16, height = 8, units = c("cm"), dpi = 300)

Waves (as curves):

# as curves: 
lwd_all <- .75

ggplot(df) +
  stat_function(fun = dnorm, args = list(mean = 300, sd = 275), lwd = lwd_all, col = "black", alpha = .99) + 
  stat_function(fun = dnorm, args = list(mean =   0, sd = 300), lwd = lwd_all, col = seeblau, alpha = .40) + 
  stat_function(fun = dnorm, args = list(mean = 600, sd = 250), lwd = lwd_all, col = "black", alpha = .40) +
  stat_function(fun = dnorm, args = list(mean = 900, sd = 225), lwd = lwd_all, col = seeblau, alpha = .75) +
  scale_x_continuous(limits = c(-4000, 1700)) +
  theme_void() # +

  # annotate(geom = "text", x = -3900, y = .0016, label = course_title, color = "black", alpha = .90, adj = 0, fontface = 2, size = 2.5)

ggsave("pix/wave/waves_4.png", width = 15, height = 1.5, units = c("cm"), dpi = 300)

ToC art

ToC bar chart

ToC (chronology and relevance of sessions) as bar charts:

# N <- 10  # number of chapters/topics
# nr <- 1:N
# tp <- c("Introduction", "Chapter 2", "Chapter 3", "Chapter 4", "Chapter 5", 
#         "Chapter 6", "Chapter 7", "Chapter 8", "Chapter 9", "Chapter 10")
# val <- 10 + nr

# Table of contents (ToC):
toc <- tribble(
  ~nr, ~tp,                 ~ctr,   
  0,  "Introduction",          3,  
  1,    "Basic R",              10,  # was: "Basic R concepts and commands",
  2,    "Visualizing data",      8, 
  3,    "Transforming data",     9, 
  4,    "Exploring data",       10,  # was: "Exploring data (EDA)"
  5,    "Tibbles",               7,
  6,    "Importing data",        5, 
  7,    "Tidying data",          9, 
  8,    "Joining data",          8, 
  9,    "Functions",             9,
 10,  "Iteration",             7
)

toc <- toc %>% mutate(nr_val = nr + 10) # add constant to increase overall height of bars

# Parameters:
N <- nrow(toc)
max_nr_val <- max(toc$nr_val)
min_ctr <- min(toc$ctr)
max_ctr <- max(toc$ctr)
tol <- 4  # tolerance value (for text labels)
txt_size <- 2.5  # size of txt labels (below)

## Colors: ------ 

# unikn.pal  # basic 

unikn_toc <- c(rev(unikn.pal[5:10]), # white:black (6) 
               unikn.pal[1:4],       # seeblau1 to 4 (4)
               seeblau)              # seeblau4 (1)
# unikn_toc  # 11 colors

Bar plots:

## Bar plots: ------ 

# (a) Chronology:

bar_hori <- ggplot(toc, aes(x = nr)) +
  geom_bar(aes(y = ctr, fill = nr_val), stat = "identity", color = grey(.5, 1)) +
  scale_fill_gradientn(colors = unikn_toc) +
  geom_text(aes(y = ctr + .25, label = tp), angle = 0, adj = 0, size = txt_size) + 
  scale_x_reverse(breaks = 0:N, labels = 0:N) + 
  # scale_x_continuous(breaks = 0:N, labels = 0:N) + 
  scale_y_continuous(limits = c(0, (max_ctr + tol)), breaks = 1:max_ctr, labels = 1:max_ctr) + 
  theme_minimal() + 
  theme(legend.position = "none", panel.grid.minor = element_blank(), panel.grid.major.x = element_blank()) +
  coord_flip() + 
  labs(title = "ds4psy: Chronology of sessions", x = "Session", y = "Relevance")
bar_hori


# Save plot:
cur_name <- paste0(pic_path, "toc/", "toc_chronology", ".png")
ggsave(cur_name, width = 15, height = 10, units = c("cm"), dpi = 300)

# (b) Relevance:

bar_vert <- ggplot(toc, aes(x = nr)) +
  geom_bar(aes(y = ctr, fill = ctr), stat = "identity", color = grey(.5, 1)) +
  # scale_fill_gradientn(colors = unikn_toc) +
  scale_fill_gradient(low = "white", high = seeblau, limits = c(min_ctr, max_ctr)) +
  geom_text(aes(y = ctr + .5, label = tp), angle = 90, adj = 0, size = txt_size) + 
  scale_x_continuous(breaks = 0:N, labels = 0:N) + 
  scale_y_continuous(limits = c(0, (max_ctr + tol)), breaks = 1:max_ctr, labels = 1:max_ctr) + 
  theme_minimal() + 
  theme(legend.position = "none", panel.grid.minor = element_blank()) +
  labs(title = "ds4psy: Relevance of topics", x = "Session", y = "Relevance")
bar_vert


# Save plot:
cur_name <- paste0(pic_path, "toc/", "toc_relevance", ".png")
ggsave(cur_name, width = 15, height = 12, units = c("cm"), dpi = 300)

ToC polar charts

ToC and current session as a clock chart (bar chart with polar coordinates):

# (2) Plots on polar coordinates:

tol <- 4  # tolerance value (for text labels)

## (A) Chronology (as clock):

topic_clock <- ggplot(toc, aes(x = nr)) +
  geom_bar(aes(y = ctr, fill = nr_val), stat = "identity", color = grey(0, 1), size = .25) +
  scale_fill_gradientn(colors = unikn_toc) +
  # scale_fill_gradient(low = "white", high = seeblau, limits = c(min_ctr, max_ctr)) +
  geom_text(aes(y = max_ctr + tol/2, label = tp), adj = .5, size = txt_size) + 
  scale_x_continuous(breaks = 0:N, labels = 0:N) + 
  scale_y_continuous(limits = c(0, (max_ctr + tol)), breaks = 1:max_ctr, labels = 1:max_ctr) + 
  coord_polar() + 
  # labs(title = "Centrality of topic", x = "Chapter", y = "Topic") + 
  # theme_void() + 
  theme_light() + 
  theme(legend.position = "none", axis.line = element_blank(), 
        axis.title = element_blank(), axis.text.y = element_blank(),
        axis.ticks = element_blank(), panel.border = element_blank())
# topic_clock

## (B) Centrality of topic (as clock):

centrality <- ggplot(toc, aes(x = nr)) +
  geom_bar(aes(y = ctr, fill = ctr), stat = "identity", color = grey(.10, 1), size = .25) +
  # scale_fill_gradientn(colors = unikn_toc) +
  scale_fill_gradient(low = "white", high = seeblau, limits = c(min_ctr, max_ctr)) +
  geom_text(aes(y = (max_ctr + .65 * tol), label = tp), adj = .5, size = txt_size) + 
  scale_x_continuous(breaks = 0:N, labels = 0:N) + 
  scale_y_continuous(limits = c(0, (max_ctr + tol)), breaks = 1:max_ctr, labels = 1:max_ctr) + 
  coord_polar() + 
  # theme_void() + 
  theme_light() + 
  theme(#legend.position = "none", 
        axis.line = element_blank(), 
        axis.title = element_blank(), axis.text.y = element_blank(),
        axis.ticks = element_blank(), panel.border = element_blank()) +
  labs(fill = "Relevance:") 
centrality


# Save plot:
cur_name <- paste0(pic_path, "toc/", "centrality", ".png")
ggsave(cur_name, width = 15, height = 15, units = c("cm"), dpi = 300)

Related ideas:

Add an outer ring with global structure:
- Basics (0–1)
- EDA (2–4)
- Data tables (5–8)
- Programming (9–10)
Re-sort topics (e.g., move sessions on tibbles and importing data forward)

Current session clock

Highlight current session in polar chart:

# (C) Highlight the current session (in gold):

# session number:
n_rand <- sample(0:11, size = 1, replace = TRUE)  # random integer (0:11)
nr_session <- n_rand  # random session nr (as in polar clock plot above)
# nr_session <- 0     # specific session nr

tol <- 8         # increase tolerance value (to leave more space for text labels)
txt_size <- 2.0  # decrease (to keep topic labels within highlighted areas)
  
# Add current session_val to toc:
toc$session_val <- 0  # initialize session_val
toc$session_val[(nr_session + 1)] <- (max_ctr + tol)  # maximum y-value
toc
#> # A tibble: 11 x 5
#>       nr tp                  ctr nr_val session_val
#>    <dbl> <chr>             <dbl>  <dbl>       <dbl>
#>  1     0 Introduction          3     10           0
#>  2     1 Basic R              10     11           0
#>  3     2 Visualizing data      8     12          18
#>  4     3 Transforming data     9     13           0
#>  5     4 Exploring data       10     14           0
#>  6     5 Tibbles               7     15           0
#>  7     6 Importing data        5     16           0
#>  8     7 Tidying data          9     17           0
#>  9     8 Joining data          8     18           0
#> 10     9 Functions             9     19           0
#> 11    10 Iteration             7     20           0

cur_session_clock <- ggplot(toc, aes(x = nr)) +
  geom_bar(aes(y = session_val), stat = "identity", fill = "gold", alpha = .75) +
  geom_bar(aes(y = ctr, fill = ctr), stat = "identity", color = grey(.10, 1), size = .25) +
  # scale_fill_gradientn(colors = unikn_toc) +
  scale_fill_gradient(low = "white", high = seeblau, limits = c(min_ctr, max_ctr)) +
  geom_text(aes(y = (max_ctr + .45 * tol), label = tp), adj = .5, size = txt_size, fontface = 1) +    # topic labels
  scale_x_continuous(breaks = 0:N, labels = 0:N) + 
  scale_y_continuous(limits = c(0, (max_ctr + tol)), breaks = 1:max_ctr, labels = 1:max_ctr) + 
  coord_polar() + 
  # theme_void() + 
  theme_light() + 
  theme(legend.position = "none", 
        axis.line = element_blank(), 
        axis.title = element_blank(), axis.text.y = element_blank(),
        axis.ticks = element_blank(), panel.border = element_blank()) +
  labs(fill = "Centrality:") 
cur_session_clock


# Save plot:
cur_name <- paste0(pic_path, "toc/", "m_clock_", nr_session, ".png")
ggsave(cur_name, width = 10, height = 10, units = c("cm"), dpi = 300)

Miscellaneous R art

See https://www.r-graph-gallery.com/portfolio/data-art/ (and links below) for many more examples.

Matrix

# plotting parameters: 
opar <- par(no.readonly = TRUE)  # all par settings that can be changed.

Original version:

# Source: http://www.r-graph-gallery.com/56-matrix-abstract-painting/

# generate pairs of x-y values
nx = 100
ny = 80
x = sample(x = 1:nx, size = 90, replace = TRUE)
y = seq(-1, -ny, length = 90)
 
# set graphical parameters: 
op = par(bg = "black", mar = c(0, 0.2, 0, 0.2))
 
# plot: 
plot(1:nx, seq(-1, -nx), type = "n", xlim = c(1, nx), ylim = c(-ny+10, 1))
for (i in seq_along(x))
{
  aux = sample(1:ny, 1)
  points(rep(x[i], aux), y[1:aux], pch = sample(letters, aux, replace = TRUE), 
         col = hsv(0.35, 1, 1, runif(aux, 0.3)), cex = runif(aux, 0.3))
}
 
# signature: 
legend("bottomright", legend = "© Gaston Sanchez", bty = "n", text.col = "gray70")

Adpated version:

# Source: http://www.r-graph-gallery.com/56-matrix-abstract-painting/
    
# generate pairs of x-y values
nx <- 100
ny <-  80

x <- sample(x = 1:nx, size = (nx - 10), replace = TRUE)
y <- seq(-1, -ny, length = (nx - 10))

# plotting parameters: 
op <- par(bg = "black", mar = c(0, 0.2, 0, 0.2))

seeblau_rgb <- col2rgb(seeblau)
seeblau_hsv <- rgb2hsv(seeblau_rgb)
unikn_sample <- c("d", "s", "4", "p", "s", "y",  
                  "3", "7", "9")

# Create empty plot:
plot(1:nx, seq(-1, -nx), type = "n", xlim = c(1, nx), ylim = c(-ny + 10, 1))

# Loop:
for (i in seq_along(x))
{
  aux = sample(1:ny, 1)
  points(rep(x[i], aux), y[1:aux], pch = sample(unikn_sample, aux, replace = TRUE), 
         col = hsv(seeblau_hsv[[1]], 1, 1, alpha = runif(aux, 0.3)), cex = runif(aux, 0.3))
}

## Save plot manually (as not a ggplot object)! 

## Clean up: 
par(opar)  # restore original plot settings

Rings

Original version:

# Source: http://www.r-graph-gallery.com/58-saturn-rings-abstract-painting/
    
# generate pairs of x-y values
x = seq(-50, 50, by = 1)
y = -(x^2)
 
# set graphic parameters
op = par(bg = 'black', mar = rep(0.5, 4))
 
# Plot 
plot(y, x, type = 'n')
lines(y, x, lwd = 2*runif(1), col = hsv(0.08, 1, 1, alpha = runif(1, 0.5, 0.9)))
for (i in seq(10, 2500, 10))
{
  lines(y-i, x, lwd = 2*runif(1), col = hsv(0.08, 1, 1, alpha = runif(1, 0.5, 0.9)))
}
for (i in seq(500, 600, 10))
{
  lines(y - i, x, lwd = 2*runif(1), col = hsv(0, 1, 1, alpha = runif(1, 0.5, 0.9)))
}
for (i in seq(2000, 2300, 10))
{
  lines(y - i, x, lwd = 2*runif(1), col = hsv(0, 1, 1, alpha = runif(1, 0.5, 0.9)))
}
for (i in seq(100, 150, 10))
{
  lines(y - i, x, lwd = 2*runif(1), col = hsv(0, 1, 1, alpha = runif(1, 0.5, 0.9)))
}
 
# signature
legend("bottomright", legend="© Gaston Sanchez", bty = "n", text.col="gray70")

Adapted version:

# Adapted from: http://www.r-graph-gallery.com/58-saturn-rings-abstract-painting/
    
# generate pairs of x-y values:
x = seq(-50, 50, by = 1)
y = -(x^2)
 
# set graphic parameters:
op = par(bg = "black", mar = rep(.5, 4))
# op = par(bg = "white", mar = rep(.5, 4))

original_h <- .08  # original hue value
# Seeblau versions:
seeblau_rgb <- col2rgb(seeblau)
seeblau_hsv <- rgb2hsv(seeblau_rgb)
seeblau_hsv 
#  seeblau
# h 0.5409226
# s 1.0000000
# v 0.8784314

seeblau_h <- seeblau_hsv[[1]]

white_rgb <- col2rgb("white")
white_hsv <- rgb2hsv(white_rgb)
white_hsv  # 0 0 1

grey_rgb <- col2rgb("grey")
grey_hsv <- rgb2hsv(grey_rgb)
grey_hsv  # 0 0 0.75

# Plot: 
plot(y, x, type = 'n')

# one: 
lines(y, x, lwd = 2 * runif(1), col = hsv(seeblau_h, 1, 1, alpha = runif(1, .5, .9)))

# all: 
for (i in seq(10, 2500, 10)) {
  lines(y-i, x, lwd = 2 * runif(1), col = hsv(seeblau_h, 1, 1, alpha = runif(1, .5, .9)))
}

# inner: 
for (i in seq(500, 600, 10)) {
  lines(y - i, x, lwd = 2 * runif(1), col = hsv(0, 0, .75, alpha = runif(1, .5, .9)))
}

# middle:
for (i in seq(2000, 2300, 10)) {
  lines(y - i, x, lwd = 2 * runif(1), col = hsv(0, 0, .75, alpha = runif(1, .5, .9)))
}

# outer:
for (i in seq(100, 150, 10)){
  lines(y - i, x, lwd = 2 * runif(1), col = hsv(0, 0, .75, alpha = runif(1, .5, .9)))
}

## Save plot manually (as not a ggplot object)! 

## Clean up: 
par(opar)  # restore original plot settings

Grasslands

Original version:

# Source: https://www.r-graph-gallery.com/138-green-world-data-art/

library(ggplot2)
library(RColorBrewer)

set.seed(92)
ngroup=20
names=paste("G_",seq(1,ngroup),sep="")
DAT=data.frame()

for(i in seq(1:50)){
    data=data.frame( matrix(0, ngroup , 3))
    data[,1]=i
    data[,2]=sample(names, nrow(data))
    data[,3]=prop.table(sample( c(rep(0, 100), c(1:ngroup)), nrow(data)))
    DAT=rbind(DAT,data)
    }

colnames(DAT) = c("Year","Group","Value")
DAT = DAT[order(DAT$Year, DAT$Group), ]

ggplot(DAT, aes(x=Year, y=Value, fill=Group )) + 
    geom_area(alpha=1 , color="transparent" )+
    theme_bw() +
    scale_fill_brewer(palette="Greens", breaks=rev(levels(DAT$Group)))+
     theme(line = element_blank(),
        text = element_blank(),
        title = element_blank(),
        legend.position = "none",
        panel.border = element_blank(),
        panel.background = element_blank())

Adapted version:

# Adapted from: https://www.r-graph-gallery.com/138-green-world-data-art/

library(ggplot2)
library(RColorBrewer)

set.seed(101)

ngroup <- 20
names <- paste("G_", seq(1, ngroup), sep = "")
DAT <- data.frame()

# Color palette: 
seeblau_pal <- sample(unikn.pal, size = ngroup, replace = TRUE)
seeblau_pal <- rep(c(unikn.pal[1], unikn.pal[2], unikn.pal[3], unikn.pal[4], unikn.pal[5]), 4)

for(i in seq(1:50)){
    data = data.frame( matrix(0, ngroup , 3))
    data[ , 1] = i
    data[ , 2] = sample(names, nrow(data))
    data[ , 3] = prop.table(sample( c(rep(0, 100), c(1:ngroup)), nrow(data)))
    DAT = rbind(DAT, data)
    }

colnames(DAT) <- c("x", "group", "value")
DAT <- DAT[order(DAT$x, DAT$group), ]

dim(DAT)

ggplot(DAT, aes(x = x, y = value, fill = group)) + 
  geom_area(alpha = 1, color = "transparent") +
  theme_bw() +
  scale_fill_brewer(palette = "Blues", breaks = rev(levels(DAT$group))) +
  theme(line = element_blank(),
        text = element_blank(),
        title = element_blank(),
        legend.position = "none",
        panel.border = element_blank(),
        panel.background = element_blank()
  )

## Save plot:
# cur_name <- paste0(pic_path, "art_grass", ".png")
# ggsave(cur_name, width = 15, height = 10, units = c("cm"), dpi = 300)

Harmonographs

Original version:

# Source: https://fronkonstin.com/2014/10/13/beautiful-curves-the-harmonograph/ 

f1 = jitter(sample(c(2,3),1)); f2 = jitter(sample(c(2,3),1)); f3 = jitter(sample(c(2,3),1)); f4 = jitter(sample(c(2,3),1))

d1 = runif(1,0,1e-02); d2=runif(1,0,1e-02); d3=runif(1,0,1e-02); d4=runif(1,0,1e-02)
p1 = runif(1,0,pi); p2=runif(1,0,pi); p3=runif(1,0,pi); p4=runif(1,0,pi)
xt = function(t) exp(-d1*t)*sin(t*f1+p1)+exp(-d2*t)*sin(t*f2+p2)
yt = function(t) exp(-d3*t)*sin(t*f3+p3)+exp(-d4*t)*sin(t*f4+p4)
t=seq(1, 100, by=.001)
dat = data.frame(t=t, x=xt(t), y=yt(t))
with(dat, plot(x, y, type="l", xlim =c(-2,2), ylim =c(-2,2), xlab = "", ylab = "", xaxt='n', yaxt='n'))

Adapted version:

# Adapted from: https://fronkonstin.com/2014/10/13/beautiful-curves-the-harmonograph/ 

seed <- 202
set.seed(seed)

f1 = jitter(sample(c(2, 3), 1))  
f2 = jitter(sample(c(2, 3), 1))  
f3 = jitter(sample(c(2, 3), 1))  
f4 = jitter(sample(c(2, 3), 1))

d1 = runif(1, 0, 1e-02)  
d2 = runif(1, 0, 1e-02)  
d3 = runif(1, 0, 1e-02)  
d4 = runif(1, 0, 1e-02)

p1 = runif(1, 0, pi)  
p2 = runif(1, 0, pi)  
p3 = runif(1, 0, pi)  
p4 = runif(1, 0, pi)

t = seq(1, 100, by = .001)

xt = function(t) exp(-d1 * t) * sin(t * f1 + p1) + 
                 exp(-d2 * t) * sin(t * f2 + p2)

yt = function(t) exp(-d3 * t) * sin(t * f3 + p3) + 
                 exp(-d4 * t) * sin(t * f4 + p4)

df = data.frame(t = t, x = xt(t), y = yt(t))
df

## Plot: ------ 

with(df, plot(x, y, type = "l", 
              col = seeblau,  
              xlim = c(-2, 2), ylim = c(-2, 2), 
              xlab = "", ylab = "", 
              xaxt = 'n', yaxt = 'n'))

## Add text label: ------ 

add_label <- TRUE

if (add_label) {
  cur_lbl <- "Session X: Title of current topic"
  
  x_pos <- -2.12  # min(df$x)
  y_pos <-  2.33  # max(df$y)
  
  text(x = x_pos, y = y_pos, labels = cur_lbl, col = grey(.33, 1), adj = 0, font = 1, 
       xpd = TRUE  # FALSE = plot region; TRUE = figure region; NA = device region.
       )
}

Related ideas:

Always combine 2 similar shapes:
- wirr: perceptually random version
- klar: seemingly systematic version
Remove frame (or make a square)
Consider adding chapter numbers and titles?

Circles

Based on geometric data art from https://www.r-graph-gallery.com/186-marcus-volz-dataart/:

# Source: https://www.r-graph-gallery.com/186-marcus-volz-dataart/ 

library(dplyr)
library(ggplot2)
library(ggthemes)
 
n <- 20000
a <- 0.5
 
set.seed(101)
 
make_circle <- function(tx, ty) {
data <- data.frame(angle = runif(n, 0, 2 * pi)) %>%
mutate(x = a * cos(angle) + tx, y = a * sin(angle) + ty) %>%
select(x, y)
data2 <- data[sample(nrow(data)),]
data <- bind_cols(data, data2)
names(data) <- c("x1", "y1", "x2", "y2")
data
}
 
data <- rbind(make_circle(cos(0), sin(0)),
make_circle(cos(pi/4), sin(pi/4)),
make_circle(cos(pi/2), sin(pi/2)),
make_circle(cos(3*pi/4), sin(3*pi/4)),
make_circle(cos(pi), sin(pi)),
make_circle(cos(5*pi/4), sin(5*pi/4)),
make_circle(cos(3*pi/2), sin(3*pi/2)),
make_circle(cos(7*pi/4), sin(7*pi/4)))
 
ggplot() +
geom_segment(aes(x = x1, xend = x2, y = y1, yend = y2), data %>% filter(x1 != x2),
alpha = 0.15, size = 0.015) +
coord_equal() +
theme_tufte() +
theme(line = element_blank(),
axis.ticks = element_blank(),
axis.ticks.length = unit(0, "null"),
axis.title = element_blank(),
axis.text = element_blank(),
text = element_blank(),
legend.position = "none",
legend.margin=unit(0, "null"),
panel.background = element_blank(),
panel.border=element_blank(),
panel.grid = element_blank(),
panel.margin=unit(c(0,0,0,0), "null"),
plot.background = element_blank(),
plot.margin=unit(c(0,0,0,0), "null"),
strip.text = element_blank())

Adapted version:

# Adapted from: https://www.r-graph-gallery.com/186-marcus-volz-dataart/ 

library(dplyr)
library(ggplot2)
library(ggthemes)

data <- NULL
n <- 20000  # Original = 20000 (density/plasticity)
a <- .50    # Original = .50   (angle/distance: <.50 = more space between circles, >.50 = more overlap)

## Note special values:
# a <- 1.0     # flower
# a <- .50     # moderate overlap
# a <- .3825   # just touching circles

set.seed(101)

# Function to make (and mark) circle: 
make_circle <- function(tx, ty, n, val) {
  
  data_1 <- data.frame(angle = runif(n, 0, 2 * pi)) %>%
    mutate(x = a * cos(angle) + tx, 
           y = a * sin(angle) + ty) %>%
    select(x, y)
  
  data_2 <- data_1[sample(nrow(data_1)), ]
  
  data_3 <- data.frame(rep(val, n))  # mark current circle (with nr)
  
  data <- bind_cols(data_1, data_2, data_3)
  names(data) <- c("x1", "y1", "x2", "y2", "val")
  
  data
}

## Create data: ------ 
## for different circles (and mark with val): 
data <- rbind(make_circle(cos(0), sin(0), n, 1),                 # 1:  0 (top)       = 1
              make_circle(cos(1 * pi/4), sin(1 * pi/4), n, 6),   # 2:  8 (top-right) = 2
              make_circle(cos(1 * pi/2), sin(1 * pi/2), n, 3),   # 3: 15 (right)     = 3
              make_circle(cos(3 * pi/4), sin(3 * pi/4), n, 4),   # 4: 52 (top-left)  = 8
              make_circle(cos(1 * pi/1), sin(1 * pi/1), n, 7),   # 5: 45 (left)      = 7
              make_circle(cos(5 * pi/4), sin(5 * pi/4), n, 2),   # 6: 38 (bot-left)  = 6
              make_circle(cos(3 * pi/2), sin(3 * pi/2), n, 5),   # 7: 30 (bot)       = 5
              make_circle(cos(7 * pi/4), sin(7 * pi/4), n, 8)    # 8: 22 (bot-right) = 4
)

# head(data)
# dim(data)

## Filter data (optional?): ----- 
# data <- data %>% filter(x1 != x2)
# dim(data)

## Colors: ------ 
# unikn.pal  # basic color scale
col_light <- unikn.pal[4] # seeblau 

# col_dark  <- unikn.pal[5] # black 
# col_dark  <- unikn.pal[6] # dark grey
col_dark  <- unikn.pal[7] # mid grey


# Plot: ------ 
ggplot(data) +
  geom_segment(aes(x = x1, xend = x2, y = y1, yend = y2, col = val), # color by val
               alpha = .20, size = .015) +
  scale_color_gradient(low = col_light, high = col_dark) +  # color scale for val 
  coord_equal() +
  theme_nothing()

Note

perceived plasticity jointly depends on density (n), transparency (alpha), and color value (val).
at some resolutions, the color in overlap regions (seeblau and dark grey) appears to be green!

Ideas

choose subsets of the set of 8 complete circles
vary the number and location of circles
map circle number val to a color scale

ToDo

create other objects (squares, triangles, diamonds, etc.)

Examples

4 circles

Positions and color values can be varied:

8 circles

Colors can be sorted or mixed up:

Tunnel

# Source: Päivi Julin at <https://www.r-graph-gallery.com/206-paivi-julin/>
library(grid) 
 
dev.new()
pushViewport(viewport(width=1, height=1, angle=0, name="vp1"))
grid.rect()
 
for(i in 1:54){
    pushViewport(viewport(width=0.95, height=0.95, angle=5, name="vp1"))
    grid.rect()
    }

Pi chart

Original version:

# Source: Päivi Julin at <https://www.r-graph-gallery.com/206-paivi-julin/>

data<-numeric()
data<-readLines("http://www.apup.org/images/digits100000.txt")        # orig. data source http://www.geom.uiuc.edu/~huberty/math5337/groupe/digits.html

# subset of 10'000 digits
dataA<-numeric()
dataA<-substr(data,1,10000)

# colors
pal <- colorRampPalette(c("#f2f2f2", "blue"))
colors <- pal(10000)

# image settings. here manually set limits (by visual eye).
dev.new(height=7,width=7)
par(bg="black", mar=c(0,0,0,0))
plot(x=0,y=0,xlim=c(-50,70), ylim=c(-100,40))

# initial values
a_x <- a_y <- numeric()
a_x <- 0; a_y <- 0;

# loop decimals. each digit (0-9) represents an individual angle.
for(i in 1:nchar(dataA)){
    a<-b_x<-b_y<-numeric()
    a<-as.numeric(substr(dataA,i,i))

    b_x<-a_x+sin((2*pi/10)*(a))    # orig. y pos. + change    
    b_y<-a_y+cos((2*pi/10)*(a))    # orig. x pos. + change
    
    # draw points. change colors.
    points(x=c(a_x,b_x),y=c(a_y,b_y),type="l", col=colors[i])
    points(x=c(b_x),y=c(b_y),pch=19, col=colors[i],cex=0.3)    
    
    # initialize next round
    a_x<-b_x
    a_y<-b_y
}

text(x=40, y=0, labels=expression(pi), col="#f2f2f2", cex=12, pos=4)    # title
text(x=52, y=10.6, labels="10000", col="black",cex=0.7, pos=4)            # decimal amount

Adapted version:

# Source: Päivi Julin at <https://www.r-graph-gallery.com/206-paivi-julin/>

# Data: ------ 

N <- 10000  # set N

## pi data: 
# pi_all <- numeric()
# pi_all <- readLines("http://www.apup.org/images/digits100000.txt")        
# orig. data source http://www.geom.uiuc.edu/~huberty/math5337/groupe/digits.html
pi_all <- readLines("./data/_pi/pi_100k.txt")  # my version, based on http://www.geom.uiuc.edu/~huberty/math5337/groupe/digits.html
# head(pi_all)  # is a string

## N digits of pi (as text string): 
# pi_N <- numeric()
pi_N <- paste0(substr(pi_all, 1, 1), substr(pi_all, 3, (N - 1)))  # skip the "." at position 2!  
# substr(pi_N, 1, 10)

## Colors: ------ 

# pal <- colorRampPalette(c("#f2f2f2", "blue"))

## Based on: 
unikn.pal
# pal <- colorRampPalette(c("white", seeblau))  # on black
pal <- colorRampPalette(c(unikn.pal[[4]], grey(.35, 1)))  # on white

pi_col <- pal(N)


# Image settings: ------

# dev.new(height = 7, width = 7)

# par(bg = "black", mar = c(0, 0, 0, 0))
par(bg = "white", mar = rep(.20, 4))

x_min <-  -100
x_max <-   165
y_min <-  -165
y_max <-   100

plot(x = 0, y = 0, type = "n", 
     xlim = c(x_min, x_max), ylim = c(y_min, y_max),
     xlab = "", ylab = "", 
     xaxt = 'n', yaxt = 'n'
     )
     
# initial values:
a_x <- a_y <- numeric()
a_x <- 0 
a_y <- 0

# Loop: each digit (0-9) represents an individual angle: 
for(i in 1:nchar(pi_N)){
  
    # b_x <- b_y
    # a   <- b_x
    a   <- as.numeric(substr(pi_N, i, i))

    scale <- 2.0  # Constant scaling factor. Original: scale <- 1.0  
    
    # Next points: 
    b_x <- a_x + (scale * sin((2 * pi/10) * a))  # orig. y pos. + change    
    b_y <- a_y + (scale * cos((2 * pi/10) * a))  # orig. x pos. + change
    
    # Draw lines, points & change colors:
    points(x = c(a_x, b_x), y = c(a_y, b_y), type = "l", col = pi_col[i])
    points(x = c(b_x), y = c(b_y), pch = 19, col = pi_col[i], cex = 0.3)    
    
    # For next round: 
    a_x <- b_x
    a_y <- b_y
}

## Text label: ------ 
lbl_x <- +80
lbl_y <- -15

# text(x = lbl_x, y = lbl_y, labels = expression(pi), col = grey(.60, 1), cex = 8, pos = 2)  # title
# text(x = (lbl_x + 4), y = (lbl_y - 5), labels = paste0(N), col = grey(.15, 1), cex = .7, pos = 2)  # N of digits

N_k <- round(N/1000, 0)
cur_lbl <- paste0(expression(pi), " to ", N_k, "k digits")
text(x = (x_max - 10), y = y_min, labels = cur_lbl, col = grey(.33, 1), cex = .7, pos = NULL)

Ideas

Various ideas by category (or data type):

Word art

words as maps (see 4L1c3)
crossword puzzles, word search puzzles;
visualisations of text identities and similarities
replacements: l33t slang

Geographic art

tube lines
maps with color schemes

Numeric art

randomness (e.g., pi)

Organic art

networks
webs (e.g., strange attractors)
flowers (e.g., https://fronkonstin.com/)

Graphic art

paint brush effects

ds4psy Artwork

Hansjörg Neth, SPDS, uni.kn

2019 02 13

Course Coordinates

Artwork

Logo

Tiles

Tile plots (random)

Tile plots (sorted)

Tile plots (with borders and numeric labels)

Poles

Pole plots (random)

Pole plots (sorted)

Pole plots (with borders and numeric labels)

Text

Text labels

Colors

Color palettes

Plot themes

Logo

Theme

Leet slang (l33t)

Data

Tiles

Tile plots

Polar plots

Pi tiles

Combine multiple plots

Waves

ToC art

ToC bar chart

ToC polar charts

Current session clock

Miscellaneous R art

Matrix

Rings

Grasslands

Harmonographs

Circles

Note

Ideas

ToDo

Examples

4 circles

8 circles

Tunnel

Pi chart

Ideas

Word art

Geographic art

Numeric art

Organic art

Graphic art

Links